Georgios Goumas’s research while affiliated with National Technical University of Athens and other places

This paper introduces a novel quantum algorithm that is able to classify a hierarchy of classes of imbalanced Boolean functions. The fundamental characteristic of imbalanced Boolean functions is that the proportion of elements in their domain that take the value 0 is not equal to the proportion of elements that take the value 1. For every positive integer n, the hierarchy contains a class of Boolean functions defined based on their behavioral pattern. The common trait of all the functions belonging to the same class is that they possess the same imbalance ratio. Our algorithm achieves classification in a straightforward manner as the final measurement reveals the unknown function with probability 1. Let us also note that the proposed algorithm is an optimal oracular algorithm because it can classify the aforementioned functions with a single query to the oracle. At the same time we explain in detail the methodology we followed to design this algorithm in the hope that it will prove general and fruitful, given that it can be easily modified and extended to address other classes of imbalanced Boolean functions that exhibit different behavioral patterns.

Resource elasticity is one of the key defining characteristics of the Function-as-a-Service (FaaS) serverless computing paradigm. In order to provide strong multi-tenant isolation, FaaS providers commonly sandbox functions inside virtual machines (VMs or microVMs). While compute resources assigned to VM-sandboxed functions can be seamlessly adjusted on the fly, memory elasticity remains challenging, especially when scaling down. State-of-the-art mechanisms for VM memory elasticity suffer from increased reclaim latency when memory needs to be released, compounded by CPU and memory bandwidth overheads. We identify the obliviousness of the Linux memory manager to the virtually hotplugged memory as the key issue hindering hot-unplug performance, and design HotMem, a novel approach for fast and efficient VM memory hot(un)plug, targeting VM-sandboxed serverless functions. Our key insight is that by segregating virtually hotplugged memory regions from regular VM memory, we are able to bound the lifetimes of allocations within these regions thus enabling their fast and efficient reclamation. We implement HotMem in Linux v6.6 and our evaluation shows that it is an order of magnitude faster than state-of-practice to reclaim VM memory, while achieving the same P99 function latency with a model that statically over-provisions VMs.

We leverage eBPF in order to implement custom policies in the Linux memory subsystem. Inspired by CBMM, we create a mechanism that provides the kernel with hints regarding the benefit of promoting a page to a specific size. We introduce a new hook point in Linux page fault handling path for eBPF programs, providing them the necessary context to determine the page size to be used. We then develop a framework that allows users to define profiles for their applications and load them into the kernel. A profile consists of memory regions of interest and their expected benefit from being backed by 4KB, 64KB and 2MB pages. In our evaluation, we profiled our workloads to identify hot memory regions using DAMON.

Concurrent priority queues are widely used in important workloads, such as graph applications and discrete event simulations. However, designing scalable concurrent priority queues for NUMA architectures is challenging. Even though several NUMA-oblivious implementations can scale up to a high number of threads, exploiting the potential parallelism of insert operation, NUMA-oblivious implementations scale poorly in deleteMin-dominated workloads. This is because all threads compete for accessing the same memory locations, i.e., the highest-priority element of the queue, thus incurring excessive cache coherence traffic and non-uniform memory accesses between nodes of a NUMA system. In such scenarios, NUMA-aware implementations are typically used to improve system performance on a NUMA system. In this work, we propose an adaptive priority queue, called SmartPQ. SmartPQ tunes itself by switching between a NUMA-oblivious and a NUMA-aware algorithmic mode to achieve high performance under all various contention scenarios. SmartPQ has two key components. First, it is built on top of NUMA Node Delegation (Nuddle), a generic low-overhead technique to construct efficient NUMA-aware data structures using any arbitrary concurrent NUMA-oblivious implementation as its backbone. Second, SmartPQ integrates a lightweight decision making mechanism to decide when to switch between NUMA-oblivious and NUMA-aware algorithmic modes. Our evaluation shows that, in NUMA systems, SmartPQ performs best in all various contention scenarios with 87.9% success rate, and dynamically adapts between NUMA-aware and NUMA-oblivious algorithmic mode, with negligible performance overheads. SmartPQ improves performance by 1.87x on average over SprayList, the state-of-theart NUMA-oblivious priority queue.

The Sparse Matrix Vector (SpMV) multiplication kernel is a key component of many high-performance computing applications, but at the same time one of the most challenging to optimize, primarily due to its low flop-per-byte ratio and irregular memory accesses. As such, modern FPGAs, combined with High-Bandwidth Memory (HBM) modules, are much better-suited to the memory-bound nature of this kernel, compared to general purpose CPUs. Current FPGA-based approaches on SpMV support only single-precision floating point arithmetic. Moreover, they target for highly-streamed implementations that, although enhance performance, facilitate custom matrix storage formats, which (i) can increase the matrix footprint up to 3x, and (ii) drop the burden of input matrix transformation to developers. Towards widening the spectrum of FPGA-supported floating point formats for sparse algebra, this paper presents a first set of effective optimizations for double-precision SpMV hardware kernels using High-Level Synthesis (HLS) tools on HBM-equipped FPGAs. Results show that our work can provide 52.4x on average better performance compared to state-of-practice SpMV double-precision multiplication implementations on FPGAs for applications with volatile matrices, and up to 5.1x better performance-per-Watt compared to server-class CPUs.